You can deploy this model and start earning money today!

kbressem/
ct-heart-segmentation

Name: ct-heart-segmentation
Availability: InStock

Views

No views yet

CT Heart Segmentation

Two MONAI bundles for coronary artery segmentation from cardiac CT angiography (CCTA), plus a Docker-packaged end-to-end pipeline.

Bundle	Input	Output
`ct_binary_coronary_segmentation`	CCTA volume (CT)	Binary vessel mask + spline centerlines (JSON)
`ct_segmental_coronary_segmentation`	Binary vessel mask	21-class segmental labels (background + 20 segments)

Both bundles ship 5-fold cross-validation UNet weights and support single-model or ensemble inference.

Quick start

Docker (end-to-end)

bash

docker build -t ct-heart-seg .
docker run --gpus all -v /path/to/ct_images:/input -v /path/to/results:/output ct-heart-seg

Outputs land in /output/binary/ and /output/segmental/, with a pipeline.log alongside them.

Python (end-to-end)

python scripts/run_pipeline.py --input /path/to/ct_images --output /path/to/results

Bundle-by-bundle

See the per-bundle READMEs for single-fold usage, ensemble inference, training, and custom transforms.

Repository layout

ct-heart-segmentation/
├── ct_binary_coronary_segmentation/   # Bundle 1: CT -> binary mask
├── ct_segmental_coronary_segmentation/ # Bundle 2: binary mask -> 21-class labels
├── scripts/run_pipeline.py             # Chains both bundles (used by the Dockerfile)
└── Dockerfile

Weights

Model weights are hosted on HuggingFace:

bash

pip install huggingface_hub
huggingface-cli download kbressem/ct-heart-segmentation --local-dir ./

Requirements

monai>=1.3.0, torch>=2.0, scikit-image, scipy, pandas, psutil, itk. The projectmonai/monai:latest Docker base image covers everything except scikit-image (installed by the Dockerfile).

License

See LICENCE inside each bundle directory.